An anomaly is any unexpected or abnormal behaviour, event, or data pattern within a network of physical and computational components caused by data errors, cyber-attacks, hardware failures, or other unforeseen events. Anomaly detection analyses events after they occur, while anomaly prediction forecasts them before they manifest. The increasing complexity of Cyber-Physical Systems (CPS) presents challenges in fault management and vulnerability to advanced attacks, highlighting the need for early intervention through anomaly prediction. Existing anomaly prediction methods often fail due to a lack of formal guarantees required for safety-critical applications. In this paper, we introduce our anomaly prediction framework which merges the advantages of data analytics and the derivation of Linear Temporal Logic (LTL) formulas. LTL-based runtime monitoring and checking is a well-established technique efficient for tackling challenges in real-time and promptly. The framework processes historical data, clusters them to extract predictive patterns, and forms data sequences that represent these trends. These sequences are fed into an LTL learning algorithm to produce a formula that represents the pattern. This formula functions as a security property programmed into a runtime checker to verify system correctness and predict the possibility of anomalies. We evaluated our framework using three datasets collected from a cyber-physical system testbed and the experimental findings demonstrate a minimum accuracy of 90% in predicting anomalies.